The weather is unpredictable and can have a large impact on the profitability of seasonal businesses, particularly if staffing requirements are highly temperature-dependent. In this paper we describe our efforts in developing a what-if analysis tool to assist affected Small and Medium Enterprises in determining the best case scenario for timing hiring new staff and deciding the optimum length of temporary employment contracts.

This work simulated some alternatives of dynamic allocation of additional human resources in a company that produces various products from Pupunha palm. Its goal was to increase the average amount of trays produced per day in this line through a hybrid application of discrete event and agent-based simulation. Two different decision-making forms were proposed to find out which workstation should have received an additional operator. The first proposal was made on the level of occupancy of the operators, while the second one was made on the queue size. The computational model was operationally validated by comparing its results with the actual production data of the company.

A tunnel boring machine (TBM) is the primary resource in a tunnel construction project and generally its advance rate is equal to the performance rate of the whole project. Regarding previous studies, the utilization factor of TBMs is approximately 50% most of the time. The process of repair and maintenance of various parts of the machine and the logistic equipment takes 50% of the time. This case study aims to simulate the whole process of TBM tunneling in Ahwas subway project and find out how different scenarios of repair and maintenance can affect the utilization factor of the TBM. The model is developed using discrete-event simulation (DES) method.

The operation of offshore drilling platforms requires a lot of logistics: supply of platforms by platform supply vessels (PSVs), backward transportation of waste in containers and transportation of oil by tankers to export ports. The severe weather conditions of the Arctic Ocean increase the number of possible disruptions that influence the logistic system. The operation of PSVs and tankers has multiple constraints and interactions. An agent-based simulation has been developed in AnyLogic to support the strategic planning of logistics by year 2042. The presentation discusses the use of the model to determine the required number of vessels and compare different options of crude oil outbound logistic network design.

In collaboration with a Midwest Utility Provider, we developed a cyber defense econometric model via Anylogic that not only simulates the operational process of the Utility's local distribution infrastructure, but also helps to minimize the cost of implementing security. By measuring the economic impact of various cyber attacks affecting disparate components of the distribution infrastructure, it was discovered that both extremes of the paradigm (no security measures implemented vs. securing every device) were unacceptable solutions in regards to protecting the business financially.

Aim of this simulation case study is to analyze waiting times and throughput at the security checkpoint of an international medium sized airport. The simulations shall provide the airport operator with the ability to easily change main impact parameters of an airport security checkpoint e.g. to test new security procedures, a flightplan with more passengers and also to optimize the security operation schedule.

Chipotle Mexican Grill is a fast causal restaurant chain headquartered in Denver, Colorado. Founded in 1993, they now have over 1,700 locations across America, as well as internationally. Although the “integrity” of their food is a fundamental part of their business strategy, according to both of their co-CEO’s, throughput is also a key factor of focus.

Symbiotic simulation is a paradigm that emphasizes a close association between a simulation system and a physical system, which is usually beneficial to at least one of them and not necessarily detrimental to the others. Aimed at extending previous work in symbiotic simulation, this paper proposes a framework of symbiotic simulation that can be used to improve the performance of a production system controlled by an enterprise system.

Dynamic models are used to describe the spatio-temporal evolution of complex systems. It is frequently difficult to construct a useful model, especially for emerging situations such as the 2003 SARS outbreak.Here we describe the application of a modern predictor-corrector method – particle filtering – that could enable relatively quick model construction and support on-the-fly correction as empirical data arrives.

The labor-intensive nature of construction projects requires proper management and efficient utilization of labor resources. Improvement of labor productivity can enhance project performance and thereby lead to substantial time and cost savings. Several studies focused on identifying the effect of different factors on labor productivity, whereby the learning curve factor proved of paramount importance. Although previous research efforts developed models to represent the learning curve effect using traditional simulation approaches such as System Dynamics (SD) and Discrete Event Simulation (DES), none of these studies used Agent-Based Modeling (ABM) techniques. This study takes the initial steps and presents work targeted at analyzing the effect of learning on labor productivity using ABM.